A review of operational methods of variational and ensemble‐variational data assimilation
Tóm tắt
Từ khóa
Tài liệu tham khảo
BerreL PannekouckeO DesroziersG StefǎnescuSE ChapnikB RaynaudL.2007. ‘A variational assimilation ensemble and the spatial filtering of its error covariances: Increase of sample size by local spatial averaging’. InProceedings of the ECMWF Workshop on Flow‐Dependent Aspects of Data Assimilation 11–13 June 2007. ECMWF: Reading UK.
BerreL DesroziersG RaynaudL MontrotyR GibierF.2009. ‘Use of consistent operational ensemble variational assimilation to estimate and diagnose analysis and background error covariances’. InProceedings of the ECMWF Workshop on Diagnostics of Data Assimilation System Performance 15–17 June 2009. ECMWF: Reading UK.
Biegler LT, 1997, Large‐Scale Optimization with Applications: Part I: Optimization in Inverse Problems and Design
Buehner M, 2015, Seamless Prediction of the Earth System: From Minutes to Months
CourtierP FreydierC GeleynJ‐F RabierF RochasM.1991. ‘The Arpège project at Météo‐France’. InProceedings of the ECMWF Workshop on Numerical Methods in Atmospheric Models 9–13 September 1991. ECMWF: Reading UK.
Daley R, 1991, Atmospheric Data Analysis
DeeDP.2002. ‘An adaptive scheme for cycling background‐error variances during data assimilation’. InProceedings of ECMWF/GEWEX Workshop on Humidity Analysis 8–11 July 2002. ECMWF: Reading UK.
ECMWF.2015. ‘Documentation for the integrated forecasting system’.https://software.ecmwf.int/wiki/display/IFS/\unhbox\voidb@x\hboxOfficial+IFS+Documentation(accessed 12 January 2017).
Emerick A, 2012, History Matching and Uncertainty Characterization
Evensen G, 2007, Data Assimilation, The Ensemble Kalman Filter
Fertig EJ, 2008, Assimilating non‐local observations with a local ensemble Kalman Filter, Tellus, 59, 719, 10.1111/j.1600-0870.2007.00260.x
FisherM.1998. ‘Development of a simplified Kalman filter’. Technical Note 260. ECMWF: Reading UK.
FisherM.2003. ‘Background error covariance modelling’. InProceedings of the ECMWF Seminar on Recent Developments in Data Assimilation for Atmosphere and Ocean 8–12 September 2003. ECMWF: Reading UK.
FisherM AnderssonA.2001. ‘Developments in 4D‐Var and Kalman filtering’. Technical Note 347. ECMWF: Reading UK.
Fisher M, 1995, Estimating the Covariance Matrices of Analysis and Forecast Error in Variational Data Assimilation
HoganTF RosmondTE GelaroR.1992. ‘The NOGAPS forecast model: A technical description’. Report AD‐A247 216 Naval Research Laboratory: Monterey CA.www.dtic.mil/dtic/tr/fulltext/u2/a247216.pdf(accessed 12 January 2017).
HowesK.2016. Accounting for model error in four‐dimensional variational data assimilation. PhD thesis Department of Mathematics University of Reading: Reading UK.
Isaksen L, 2010, Ensemble of Data Assimilations at ECMWF
LeeM‐S BarkerD HuangW KuoY‐H.2004. ‘First guess at appropriate time (FGAT) with WRF 3D‐Var’. InPreprints for WRF/MM5 Users' Workshop 22–25 June 2004. Boulder CO.http://www.mmm.ucar.edu/mm5/workshop/ws04/Session5/Lee.Mi-Seon1.pdf(accessed 12 January 2017).
LorencAC.2013. ‘Recommended nomenclature for EnVar data assimilation methods’. In WGNE Blue Book Research Activities in Atmospheric and Oceanic Modelling section01: 7–8. WMO: Geneva Switzerland.http://www.wcrp‐climate.org/WGNE/BlueBook/2013/individual-articles/01_Lorenc_Andrew_EnVar_nomenclature.pdf(accessed 23 December 2016).
LorenzEN.1996. ‘Predictability: A problem partly solved’. In Proceedings of the Seminar on Predictability. Vol.1. ECMWF: Reading UK.
Meng Z, 2012, Encyclopedia of Atmospheric Sciences
NavonIM DaescuDN LiuZ.2005. The impact of background error on incomplete observations for 4D‐Var data assimilation with the FSU GSM. In Computational Science–ICCS 2: 837–844. Atlanta GA.
NCEP Environmental Modeling Center, The Global Forecast System (GFS) – Global Spectral Model (GSM)
OlesonKW DaiY BonanG BosilovichM DickinsonR DirmeyerP HoffmanF HouserP LevisS NiuG‐Y ThorntonP VertensteinM YangZ‐L ZengX.2004. ‘Technical description of the Community Land Model (CLM)’. Technical Note NCAR/TN‐461+STR.NCAR:Boulder CO.http://www.cgd.ucar.edu/tss/clm/distribution/clm3.0/TechNote/CLMTechNote.pdf(accessed 13 January 2017).
Palmer TN, 2010, Stochastic Physics and Climate Modelling
RhodinA.2015. ‘Multi Scale VarEnKF Localisation using Wavelets’. InPresentation at International Symposium on Data Assimilation RIKEN February 2015. Kobe Japan.http://www.data-assimilation.riken.jp/isda2015/program/pdf/8-4.pdf(accessed 13 January 2017).
SkamarockWC KlempJB DudhiaJ GillDO BarkerDM WangW PowersJG.2005. ‘A description of the advanced research WRF version 2’. Technical Note NCAR/TN‐486+STR. NCAR: Boulder CO.
UndénP RontuL JärvinenH LynchP CalvoJ CatsG CuxartJ EerolaK ForteliusC Garcia‐MoyaJA JonesC LenderlinkG McDonaldA McGrathR NavascuesB NielsenNW ØdegaardV RodriguezE RummukainenM RõõmR SattlerK SassBH SavijärviH SchreurBW SiggR TheH TijmA.2002. ‘HIRLAM‐5 scientific documentation (2002)’.http://hirlam.org/index.php/hirlam-documentation(accessed 13 January 2017).
Wunsch C, 2012, Discrete Inverse and State Estimation Problems: With Geophysical Fluid Applications